Economics and Human Biology 8 (2010) 309–319
Contents lists available at ScienceDirect
Economics and Human Biology
journal homepage: http://www.elsevier.com/locate/ehb
Assessing the impact of obesity on labor market outcomes
Maarten Lindeboom a, Petter Lundborg b,*, Bas van der Klaauw c
a
VU University Amsterdam, Tinbergen Institute, HEB, Netspar, and IZA
Lund University, VU University Amsterdam, Tinbergen Institute, IZA, Netspar, Centre for Economic Demography, and HEP
c
VU University Amsterdam and Tinbergen Institute
b
A R T I C L E I N F O
A B S T R A C T
Article history:
Received 20 November 2009
Received in revised form 16 August 2010
Accepted 23 August 2010
We study the effect of obesity on employment, using rich data from the British National
Child Development Study (NCDS). The results show a significant negative association
between obesity and employment even after controlling for a rich set of demographic,
socioeconomic, environmental and behavioral variables. In order to account for the
endogeneity of obesity, we use and assess instruments introduced by Cawley (2004); the
obesity status of biological relatives. Using parental obesity as an instrument, we show
that the association between obesity and employment is no longer significant. Similar
results are obtained in a model of first differences. We provide a number of different
checks on the instruments, by exploiting the richness of the NCDS data. The results show
mixed evidence regarding the validity of the instruments.
ß 2010 Elsevier B.V. All rights reserved.
Keywords:
Obesity
Employment
Instruments
Endogeneity
Panel data
1. Introduction
A growing literature focuses on the importance of nontraditional traits such as physical appearance, personality
and birth order on labor market success (Hamermesh and
Biddle, 1994; Mueller and Plug, 2006; Kantarevic and
Mechoulan, 2006). The literature on physical appearance
mainly considers three attributes: attractiveness, body
mass and height. To date, the effect of body mass has
received most attention, reflecting concerns about the
sharply increasing obesity rates found in most Western
countries (OECD, 2005, p. 87). Naturally, this raises the
question about any adverse labor market consequences.
Obesity increases the risk of various health problems
such as cancer, stroke, diabetes, asthma, hypertension,
depression and arthritis (Abbott et al., 1994; Pi-Sunyer,
2002), which may affect the individual’s capacity to work.
There are, however, alternative explanations for the effect
* Corresponding author at: Department of Economics, Lund University,
22007 Lund, Sweden.
E-mail addresses: [email protected], [email protected]
(P. Lundborg).
1570-677X/$ – see front matter ß 2010 Elsevier B.V. All rights reserved.
doi:10.1016/j.ehb.2010.08.004
of obesity on labor market outcomes. Employers may
discriminate against obese workers (e.g. Hamermesh and
Biddle, 1994; Baum and Ford, 2004; Rooth, 2009; Lundborg
et al., 2010). Discrimination is not always clear as in some
jobs, such as sales, physical appearance may be directly
related to productivity. Furthermore, obesity may be related
to non-desirable personality traits potentially affecting
productivity. For instance, Puhl and Brownell (2001) and
Sobal (2004) revealed public beliefs about obese people,
they are thought to be lazier and less socially and
intellectually skilled than their non-obese counterparts.
In this paper, we focus on the effect of obesity on
employment. The previous literature has primarily looked
at the effect of obesity on wages. The effects of obesity on
employment are not necessarily the same as the effect on
wages. If obese workers are less productive or if employers
discriminate against obese individuals, they may receive
lower wages but still be employed. However, if wages are
not fully flexible or if obesity causes serious health
problems, the effect of obesity on employment might be
severe. In line with this, Rooth (2009) found strong
indications of discrimination against obese workers by
measuring employer callbacks on fictitious job applications to real jobs, where pictures of an obese or non-obese
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M. Lindeboom et al. / Economics and Human Biology 8 (2010) 309–319
person were randomly assigned to similar applications.
Focusing on wages alone may therefore leave out an
important aspect of the effect of obesity on labor market
outcomes. This may hold particularly for groups that have
traditionally lower participation rates, such as females. The
literature on the effect of obesity on employment is
limited, however, and have reached mixed conclusions. In
Cawley (2000a,b) and Norton and Han (2008), no effect of
obesity on employment among US men and women is
obtained. Morris (2007), on the other hand, finds a
significant and negative effect for both genders in the UK.
Investigating the effect of obesity of labor market
outcomes is complicated by potential reversed causality
and endogeneity problems. Reversed causality may arise, for
instance, because energy dense fattening food is relatively
cheap, and lower wages or unemployment increases the
demand for such food. Furthermore, there may be unobserved characteristics that vary systematically between
obese and non-obese people, and these factors may also
affect employment. For instance, people with high discount
rates may be more prone to weight-gaining consumption. At
the same time, high discount rates make investments in
human capital (and thus future labor market outcomes) less
attractive (Cawley, 2000a,b, 2004; Baum and Ford, 2004).
We contribute to the literature on the effect of obesity
on labor market outcomes. For this purpose, we use data
from the British National Child Development Study
(NCDS), which is a longitudinal study on around 17,000
individuals born in Great Britain in the week of March 3–9,
1958, who are followed up to 2004, when they were 46
years old. The majority of studies on obesity and labor
market outcomes is conducted in a US context and we thus
contribute to the small literature examining the topic in a
British context.
The NCDS has not often been used to study the effect of
obesity on labor market outcomes. However, the data have a
number of advantages. Since NCDS follows people from
birth, it contains extensive information on early life
conditions that could potentially affect both obesity and
labor market outcomes. Even more importantly, it records
the height and weight of both the respondent and the
respondent’s mother and father, allowing us to use the
obesity status of a respondent’s parents as instruments. The
idea of using the obesity status of biological relatives as an
instrument was introduced by Cawley (2000a,b), who
argued that the strong association in body size between a
parent and a child mainly reflects genetic factors, making it
potentially useful as an instrument. The body mass index of
biological relatives has since then been used as an
instrument in a relatively large number of studies (Cawley,
2004; Burkhauser and Cawley, 2004; Cawley et al., 2005;
Brunello and D’Hombres, 2007; Kline and Tobias, 2008;
Atella et al., 2008; Shimokawa, 2008; Greve, 2008; Trogdon
et al., 2008; Renna et al., 2008; Davey Smith et al., 2009).1
1
Two recent studies have used specific genetic markers for obesity as
instruments (Norton and Han, 2008; Ding et al., 2009). Other papers have
used instruments providing non-genetic variation, such as the average
BMI of individuals in the same health authority area (Morris, 2006, 2007)
and sibling sex composition (Lundborg et al., 2007).
Using the obesity status of parents as an instrument,
instead of that of siblings or children, has certain
advantages. First, since parental obesity is available for
all respondents, we do not have to rely on the obesity of a
sibling or a child, which would restrict the sample to only
those where information on a sibling or a child is present.
Second, since we have two instruments we are able to
perform overidentification tests on our instruments. We
use these tests to assess the use of parental obesity as an
instrument. It should be noted that the NCDS data has not
been used before to estimate the causal effect of obesity by
using a biological relative’s weight as an instrument.
Using parental obesity status (or any biological relatives
obesity status) as an identification strategy depends on the
assumption that there are no other pathways than via the
respondent’s obesity status in which parental obesity
affects the respondent’s labor market outcomes. Alternative pathways may be present if genetic or non-genetic
factors which affect obesity also have a direct impact on
labor market outcomes. There is some evidence, based on
twin and adoption studies, suggesting that the association
in body weight between biological relatives is due to
genetics, and that shared environmental factors play no
role. However, this finding is not uncontroversial. We,
therefore, exploit the richness of the NCDS data, and
contribute to the literature by providing some checks for
the appropriateness of using a biological relative’s weight
as an instrument. Such tests have been rare in the
literature so far.
To assess our instrument, we start by exploiting the rich
data in the NCDS, and show that the strong association
between the respondent’s obesity status and parental
obesity is virtually unaffected when we condition on
environmental factors during childhood and adolescence.
Conditioning on these factors at the same time makes the
association between the respondent’s obesity status and
employment status weaker. This might be taken as
evidence that it is mainly genetic factors which affect
the intergenerational association in obesity. We further
test this by exploiting information on adopted children in
our data. If the association is only due to genetics, then one
would expect no association between the obesity status of
adopted children and their adoptee parents. Indeed, we
find that the coefficient for adopted children is close to
zero, again suggesting that environmental factors play a
small role. These results, in sum, provide at least suggestive
evidence that parental obesity mainly predicts genetic
variation in the respondent’s obesity status, making it
potentially useful as an instrument.
Using parental obesity as an instruments would still be
invalid, however, if the same genes that predict obesity
also predict labor market outcomes. We, therefore, also
exploit the panel feature of the NCDS and conduct
regressions in first differences. This removes all unobserved time-invariant heterogeneity, such as genetic
factors correlated with both obesity and labor market
success.
In our data there is a negative correlation between
obesity and the employment probability among women
and men. Although the sizes of these associations become
smaller after controlling for an extensive list of controls for
M. Lindeboom et al. / Economics and Human Biology 8 (2010) 309–319
cognitive ability and parental inputs, they remain negative
and significant. Using our instrumental variables strategy,
no significant effects of obesity on employment remain.
Similar results are obtained when we exploit the panel
feature and estimate the model in first differences.
The structure of the paper is as follows. Section 2
provides a general analytical framework. Section 3
introduces the National Child Development Study data
and reports on the variables used in the empirical part.
Section 4 discusses the empirical results. Section 5
concludes.
obesity should not be related to both current and past
employment shocks eit and eit1. The latter may be the case
if the type of nutrient intake depends on employment
status, for example, when low-income people have a
higher demand for relatively cheap energy dense fattening
food. Taking first differences may thus solve some
endogeneity problems, but the estimates may still suffer
from reversed causality.
According to Cawley (2004) obesity may be affected by
the employment status
OBESITY it ¼ X it d þ EMPLOYMENT it a þ Z it f þ uit þ eit :
2. Analytical framework
In this section we present an analytical framework,
which includes the larger part of the frameworks used in
the literature, such as, for example, Cawley (2004).
However, we focus on employment rather than wages
(see also Lundborg et al., 2007). This implies that we
consider another margin of labor market outcomes, where
responses to obesity may be different. For example, in a
competitive labor market with flexible wages, there may
be a very substantial wage penalty to obesity (if obese
people are, for example, less productive), which hardly
affects employment outcomes. On the other hand if obesity
affects the propensity to work in some occupations, while
being irrelevant in other occupations, one might observe
that conditionally on working obesity does not affect
wages. Even though our model framework has the same
features as models used to study wage effect, the
interpretation of the causal parameter is different.
Our outcome variable is an indicator for being
employed, and we adopt a linear probability model:
EMPLOYMENT it ¼ OBESITY it bt þ X it g þ mit þ eit ;
(1)
where OBESITYit is the obesity status of individual i at age t
and Xit is a vector of other variables affecting employment
rates. The term mit captures genetic and non-genetic factors
which may be time-varying, and eit is the residual. This
equation strongly relates to Cawley (2004), with the
exception that we allow the effect of obesity to vary with
age t. OLS estimation produces consistent estimates of bt
only in case the vector Xit contains all variables that both are
correlated with obesity and affect employment. However,
for reasons discussed in the previous section OBESITYit may
be correlated with mit.
One may consider taking first differences if one is
willing to believe that mit remains constant over time
(mit = mi). This strategy was adopted by Baum and Ford
(2004), Cawley (2004) and Cawley and Danziger (2005). If
also the effect of obesity on employment does not vary by
age, i.e. bt = b, then the first-difference equation equals
EMPLOYMENT it EMPLOYMENT it1 ¼
(3)
where Zit contains variables only affecting obesity, uit
captures genetic and non-genetic components, and eit
contains shocks. There are potential sources for endogeneity bias of obesity in the employment Eq. (1). First, the
current employment status EMPLOYMENTit may affect
current OBESITYit. This reverse causality means that a in
Eq. (3) is non-zero. Second, mit and uit may be correlated,
which implies that there are unobserved factors affecting
both obesity and employment outcomes.
Identification of the causal effect of obesity on employment should come from independent variation in obesity
status. In particular, the variable Zit should have a nontrivial effect on obesity. If we use Zit to instrument
OBESITYit in the levels Eq. (1), the identifying assumption is
that Zit should be uncorrelated with mit and also to
employment shocks eit in the employment regression.
We follow Cawley (2004) and use obesity status of
biological relatives as instrumental variables. In particular,
we use obesity of the parents of the respondent. It is well
established, through twin, adoption and family studies
that an individual’s risk of obesity is substantially
increased when he or she has relatives who are obese. A
number of studies have provided evidence, suggesting that
40–70% of the variation in obesity-related phenotypes,
such as body mass index, skinfold thickness, fat mass and
leptin levels, is inheritable (see Grilo and Pogue-Geile,
1991, for an overview).
The crucial assumption for parental obesity to be a valid
instrument for the respondent’s obesity is that it should
not have an independent effect on the respondent’s
employment outcomes. One could, however, think of a
number of pathways through which parental obesity could
affect the respondent’s employment outcomes.
First, genetic factors determining weight may be the
same, or are correlated with, genetic factors determining
labor market success. In the estimations we include several
variables correlated with parental labor market outcomes
as regressors. These variables should control for such
genetic factors, if they exist. Furthermore, estimating the
model in first differences as in Eq. (2) may solve some of
ðOBESITY it OBESITY it1 Þb
þðX it X it1 Þg þ ðeit eit1 Þ:
An important condition for obtaining consistent estimates from a first-difference estimator is that current
311
(2)
these issues as it removes time-invariant genetic effects
from the employment regression.
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M. Lindeboom et al. / Economics and Human Biology 8 (2010) 309–319
Second, one could imagine the existence of household
environment factors common to respondents and their
parents that may affect both the respondent’s employment status and obesity. Cawley (2004) argues forcefully
that all correlation in weight between biological relatives
is due to genetic factors, and cites evidence from several
studies all pointing to the non-importance of such
common household effects. It has been found, for
instance, that the correlation between the body size of
parents and their children is no different for twins reared
apart or together (e.g. Maes et al., 1997). Moreover, it has
been found that there is no significant correlation
between the body mass of unrelated adoptees reared in
the same family (Grilo and Pogue-Geile, 1991). Reviewing
the literature on genetic and environmental influences on
obesity, Grilo and Pogue-Geile (1991) concluded that the
only important environmental experiences are those not
shared among family members and that experiences
shared among family members appear largely irrelevant
in determining individual differences in weight and
obesity.
However, this is not uncontroversial. There is a strand
of literature that argues that the family environment
plays the main role in the development of children’s food
preferences. The idea is that parents shape the eating
environment of their children by making food available
and by their own eating habits and food choices (see
Birch, 1999, for a review). We have access to an
exceptionally rich dataset that allow us to deal with this
to some extent. The data has detailed information on
parental choices regarding investments in their children’s
well-being and health, such as whether the child was
breastfed, whether the parents took their child out for
walks, outings, whether the parents went to swimming
pools with their child and whether the mother and father
read frequently to the child. Furthermore there is
information on mother’s smoking behavior during
pregnancy, whether there were domestic tensions in
the family and whether there were alcohol problems in
the family. Such variables are likely to be strongly
correlated to parental preferences towards food and
eating habits. We will examine the sensitivity of our
estimates to the inclusion of these parental input
variables. Furthermore, it is not necessarily so that any
non-genetic factors potentially reflected in the relationship in obesity between a parent and child renders the
instrument invalid. If non-genetic eating preferences are
transmitted from the parent to the child, this is no
problem as long as these eating preferences are not
directly related to the labor market outcomes of the child.
It should also be noted that the food rationing implemented in Great Britain during the second world war did
not end in until 1954, when the last restrictions on the
sales of meat and bacon were lifted (Huxley et al., 2000).
The parents of the NCDS respondents thus grew up in an
environment with food rationing, where fatness could be
expected to be more dependent on genetic factors than on
excessive eating habits. Finally, our data has a subsample
of adoptees. If environmental factors are important one
would also expect to see an association in obesity
between the adopted child and their adoptee parents.
3. Data
In the empirical analyses we use data from the National
Child Development Study (NCDS), which is a longitudinal
study of about 17,000 individuals born in Great Britain in
the week of March 3–9, 1958. Originally, the NCDS started
out as the ‘‘Perinatal Mortality Survey’’, with the aim of
surveying economic and obstetric factors associated with
stillbirth and infant mortality. Since the first survey in
1958, cohort members have been traced on seven other
occasions, in 1965 (age 7), 1969 (age 11), 1974 (age 16),
1981 (age 23), 1991 (age 33), 1999/2000 (age 42), and 2004
(age 46). It should be noted, however, that we do not use
the information from the 2004 wave, since it does not
include information on height and weight.
In the 1958 wave, information was gathered from the
mother and from medical records, whereas interviews
were carried out with parents, teachers, and the school
health service in waves 1–3. In the latter waves, ability
tests were administered to the cohort members. In
subsequent surveys, information on employment and
income, health and health behavior, citizenship and values,
relationships, parenting and housing, education and
training of the respondents was included.
Since the NCDS is a long panel, attrition may be of
serious concern. Case et al. (2005) investigated attrition in
the NCDS by comparing low birth weight and father’s
occupation across the different waves. No evidence for any
non-random attrition with respect to these variables was
obtained. Moreover, advisory and user support groups of
the NCDS compared respondents and non-respondents in
the later surveys in terms of social and economic status,
education, health, housing and demography. Again, it was
found that the sample survivors did not differ from the
original sample to any great extent (National Child
Development Study User Support Group, 1991). In yet
another study, the 1981 sample was compared to the
United Kingdom 1981 Population Censuses in terms of key
variables such as marital status, gender, economic activity,
gross weekly pay, tenure and ethnicity (Ades, 1983). The
author concluded that the sample appeared to be
representative with respect to these variables.
3.1. Dependent variable: labor market outcomes
Our main dependent variable is employment. We
consider employment at age 33 and 42, defined as having
a full-time or part-time job, or being self-employed. Table
1 shows that at both ages the employment rates for men
are about 92%. For women the employment rate increased
from 69% at age 33 to 80% at age 42.
3.2. Independent variables
3.2.1. Obesity status
The NCDS records the height and weight of the
respondents at all waves, except for the last one in
2004. In the 1981 and 2000 waves, weight and height were
self-reported, while they were measured by interviewers
in the other waves. Using the measures on height and
weight, we construct the body mass index (BMI), which, in
M. Lindeboom et al. / Economics and Human Biology 8 (2010) 309–319
313
Table 1
Descriptive statistics.
Males
Mean
Females
SD
Mean
SD
Dependent variables
Employed at age 42
Employed at age 33
0.916
0.924
0.277
0.265
0.798
0.685
0.401
0.465
Obesity
Obese 42
Obese 33
Mother obese
Father obese
0.167
0.113
0.039
0.069
0.373
0.316
0.195
0.254
0.169
0.115
0.039
0.060
0.375
0.319
0.194
0.237
Demographic variables
Less than O-level
O-level equivalent
A-level equivalent
Degree equivalent
White
Married or cohabitating
Father’s years of schooling
Mother’s years of schooling
Number of children
0.228
0.189
0.299
0.133
0.920
0.806
7.502
7.592
1.468
0.420
0.391
0.458
0.339
0.271
0.396
4.510
4.384
1.349
0.238
0.281
0.263
0.110
0.923
0.810
7.530
7.655
1.763
0.426
0.450
0.440
0.313
0.266
0.392
4.490
4.397
1.364
26.543
0.040
0.078
0.063
0.027
0.005
7.521
0.195
0.268
0.243
0.163
0.071
26.536
0.053
0.086
0.063
0.032
0.005
7.624
0.224
0.281
0.244
0.176
0.071
0.299
0.642
0.464
0.337
0.520
0.515
0.443
0.778
0.674
0.080
0.458
0.479
0.499
0.473
0.500
0.500
0.497
0.416
0.469
0.272
0.316
0.658
0.457
0.338
0.577
0.520
0.468
0.803
0.655
0.103
0.465
0.474
0.498
0.473
0.494
0.500
0.499
0.398
0.475
0.304
4.990
17.221
21.431
15.693
8.282
8.178
2.796
11.110
9.172
7.342
8.679
9.026
4.857
16.622
23.391
15.644
6.205
5.711
2.685
10.480
8.635
6.896
7.445
7.158
4.486
9.145
3.898
7.844
Early life conditions
Mother’s age at birth
Financial difficulties at age 7
Financial difficulties at age 11
Financial difficulties at age 16
Domestic tension in family
Parental alcohol problems
Parental investments
Mother smoked during pregnancy
Mother breastfeeded
Mother read
Father read
Often walked with mother (age 11)
Often walked with father (age 11)
Often went swimming with parents (age 11)
Outings with mother (age 7)
Outings with father (age 7)
Low birth weight
Cognitive ability
Math score (age 7)
Math score (age 11)
Reading score (age 7)
Reading score (age 11)
BSAG score (age 7)
BSAG score (age 11)
Other
Number of cigarettes
turn, is used to construct an indicator of being obese. We
follow the convention and label someone as obese if having
a BMI of 30 or above. Since the definition of obesity varies
for children and teenagers, we use age-specific thresholds
of obesity and overweight, as provided by Cole et al.
(2000).
Previous research has shown substantial measurement
errors in self-reported height and weight (e.g. Rowland,
1989). For instance, underweight people tend to overreport their weight, while the opposite is the case for
overweight people. We correct for such errors in selfreported height and weight in the 1981 and 2000 waves by
using the results from Burkhauser and Cawley (2008),
where prediction equations for actual weight and height
were provided.2 Applying their formulas, average BMI for
females at age 42 increase from 25.29 to 25.68 and from
26.58 to 26.75 for males. This, in turn, increases the
fraction of obese females from 15.1% to 16.9% and the
fraction of obese males from 15.7% to 16.7%. The correction
for systematic measurement error might be more important in OLS regressions than in regressions in which obesity
is instrumented. It is well known that classical measure-
2
The results from Burkhauser and Cawley (2008) were obtained by
comparing self-reported weight with measured weight in the NHANES
data in the US. It should be noted that the relation between self-reported
weight and measured weight may differ between the UK and the US.
[(Fig._1)TD$IG]
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M. Lindeboom et al. / Economics and Human Biology 8 (2010) 309–319
Fig. 1. Obesity status at various ages by parental obesity status.
ment errors in regressors cause OLS estimates to be biased
towards 0, which can be solved by instrumenting the
regressors. Descriptive statistics on obesity status are
shown in Table 1.
The NCDS also includes information on the weight and
height of the respondent’s father and mother. This
information was assessed in wave 3, when the respondents
were 11 years old, and is self-reported by the mother and
father. Since we are mainly interested in the obesity of a
biological parent, we make use of information on family
relations, allowing us to discriminate between biological
parents and adoptee parents, stepparents and foster
parents. In the empirical analyses we only focus on
individuals for which both the father’s and mother’s
obesity status are recorded at age 11.
Fig. 1 shows obesity rates at various ages by the obesity
status of the mother and the father for the NCDS
respondents. Clearly, the obesity rates are greater for
those having an obese mother or father at all ages
considered. At age 7, the obesity rate of those with obese
parents is about twice as high as for those with non-obese
parents. At ages 11 and 16 the difference becomes even
greater. From age 23 and onwards, the picture suggests
that the increase in obesity is greater for those having
obese parents. However, the dip in obesity at age 23 is also
greater for those with obese parents. This dip may suggest
that the self-reports on height and weight at age 23 are
unreliable and that our measurement error correction does
not address this adequately. In the following, we will,
therefore, not use information on obesity at age 23.
Another reason for not using the data at age 23 is that at
this age some individuals were still in full-time education.
At age 42, which is the age at which we evaluate labor
market outcomes, the obesity rate of those having obese
parents is about 33%, whereas the corresponding rate for
those not having obese parents is about 15%. The figure
suggest that there are substantial differences in the
probability of being obese by the obesity status of the
parents. This also gives us the predictive power needed
from our instrument.
3.2.2. Other background variables
The NCDS contains rich information on issues such as
the individual’s initial health assets, the socioeconomic
status during early childhood and cognitive ability during
childhood. We follow Llena Nozal (2007) and Case et al.
(2005) when constructing our relevant background variables. In Table 1, sample means are shown. Since many
variables have some item non-response we follow Case et
al. (2005), and construct dummy variables that indicate if
the information on a variable is missing to avoid losing
many observations.
To measure the family’s socioeconomic status, we
include information on the number of years of education of
the mother and the father, a measure of permanent family
income at age 16, and a measure of financial problems in
the family at ages 11, 14, and 16. As to family income, the
NCDS only records it when the child is 16. Since this
measure might not reflect living standards earlier in
childhood or persistent poverty problems, the data holders
have developed a measure of family income, which we will
make use of. Since the permanent income measure is
dependent on the estimation technique and data availability, however, we will use this measure in combination
with the measures of whether or not the family had serious
financial difficulties when the child was aged 7, 11, and
16.3
We created a number of different measures of parental
inputs and early life conditions. In order to capture
mother’s smoking during pregnancy, we created a dummy
variable indicating whether the mother smoked after the
fourth month of pregnancy. Smoking during pregnancy has
been found to be related with cognitive deficiencies and
other health problems, such as low birth weight, and may
thereby affect both obesity and labor market outcomes
(see, for instance, Blair et al., 1996; Williams et al., 1998).
Moreover, the mother’s age at the child’s birth may affect
child’s health through, for instance, nutritional deficiencies
if the mother is very young, or delivery complications if the
mother is older (Llena Nozal, 2007). In addition, we include
an indicator of having low birth weight and whether the
respondent was breastfed.
A number of indicators of parental activities together
with their child were created as well. These indicated
whether the mother or the father often took the child for
walks, to swimming pools, to outings and were assessed
when the child was 11. In addition, we constructed
variables indicating whether the mother and the father
frequently did read books to the respondent at age 7.
Finally, two variables measuring the presence of any
alcohol problems among the parents and the presence of
any domestic tension during the upbringing of the child
were created.
To measure cognitive ability at early ages, we used the
results from test scores on math and reading tests at ages 7
and 11. In the math test, which was designed for the NCDS,
the score ranges from 0 to 10. Prior studies have
established that test scores at the age of 7 show a
significant impact on later education attainments and
3
Family income in the NCDS consists of father’s earnings, mother’s
earnings, and other sources of family income. In order to obtain a
prediction of permanent parental income, the data holders used
information on individual characteristics to predict permanent wages
for a sample of NCDS respondents and their fathers. For details, see Taylor
(2008).
M. Lindeboom et al. / Economics and Human Biology 8 (2010) 309–319
315
Table 2
Marginal effects and robust standard errors from OLS regression for employment at age 42 (separate for males and females).
Employment
Males
Obese 42
n
Obese 33
n
Females
(I)
(II)
(III)
(IV)
(V)
(I)
(II)
(III)
(IV)
(V)
0.023*
(0.012)
4185
0.045***
(0.016)
3543
0.019*
(0.011)
4185
0.030**
(0.014)
3543
0.017
(0.011)
4185
0.030**
(0.014)
3543
0.013
(0.010)
4185
0.025*
(0.014)
3543
0.013
(0.030)
4185
0.027*
(0.015)
3543
0.049***
(0.018)
4147
0.061***
(0.022)
3694
0.039**
(0.017)
4147
0.046**
(0.022)
3694
0.040**
(0.018)
4147
0.047**
(0.022)
3694
0.038**
(0.018)
4147
0.045**
(0.022)
3694
0.038**
(0.018)
4147
0.046**
(0.022)
3694
I: no controls; II: basic controls included (marital status, ethnicity, education, smoking, number of children); III: controls for parental background added
(permanent income, parental education financial difficulties, mother’s age at birth); IV: controls for ability added (test scores on math, reading scores scores
at ages 7 and 11, and BSAG scores); V: controls for parental inputs added (going for walks, outings, going to swimming pools, smoking during pregnancy, low
birth weight, breastfed, alcohol problems, domestic tension, mother’s smoking during pregnancy, and reading to child.
*
p < 0.1.
**
p < 0.05.
***
p < 0.01.
labor market outcomes (Currie and Thomas, 2001). Reading skills were assessed by the Southgate Reading Test.
Social maladjustement was assessed at ages 7 and 11
with the Bristol Social Adjustment Guide (BSAG) study. The
BSAG consists of a large number of behavioral items, such
as ‘attitudes to teacher’, ‘attitudes towards other children’,
evaluated by the child’s teacher. Higher scores indicate
higher maladjustment.
Education is measured through four dummy variables,
indicating national vocational qualification levels. The
following categories were included: less than O-levels, Olevel equivalent, A-level equivalent, and degree equivalent.
This follows the definition used by Llena Nozal (2007). To
capture marital status, we created a dummy variable
indicating whether the respondent was married or
cohabitating. Ethnicity was measured through a dummy
variable, indicating whether the respondent belonged to
the European Caucasian, group. Additional covariates were
the number of children and the number of cigarettes
smoked per day.
4. Results
4.1. Baseline OLS results
We start by examining whether there exist any
evidence suggesting different employment levels by
obesity status in our data. Table 2 summarizes the results
from regressions on the association between obesity and
employment at age 42. The first column of the tables shows
the results when we include only the individual’s obesity
status as covariate. In columns (II) to (V) we then add
various sets of control variables. It should be noted that the
results in the different rows reflect separate regressions on
the effect of obesity at various ages on the outcomes at age
42.
Starting with only obesity as a covariate (columns (I)),
the results show that both obese males and females face
significantly lower employment probabilities. Being obese
at age 42 is associated with a 4.9 percentage points
reduction in the employment probability for females and a
2.3 percentage points reduction for males. Even stronger
results are obtained when measuring obesity at age 33,
with the obesity penalty doubling for males and increasing
by about 20% for females.
4.2. Accounting for heterogeneity
4.2.1. Controlling for socioeconomic characteristics
The results discussed so far did not control for other
characteristics of the individual that may be correlated
with obesity. In the columns (II) of Table 2, we, therefore,
add some basic control variables, including marital status,
ethnicity, education, number of children and smoking. For
both males and females, the negative association between
obesity and employment is still significant for both males
and females, although it is somewhat reduced in magnitude. The pattern is the same, irrespective if obesity is
measured at age 33 or age 42.
4.2.2. The influence of family background
Obese persons may come from other types of family
backgrounds than their non-obese counterparts. Coming
from a background with lower economic and human
capital, for instance, may also affect later labor market
outcomes. Thus, the previously found employment ‘‘gap’’
by obesity status may simply reflect such differences. In
order to account for differences in family background, we
include controls for parental education, permanent
income, and financial difficulties in the household at ages
7, 11, and 16. As shown in columns (III) of Table 2, the
association between obesity and employment does not
change when controlling for family background.
4.2.3. What role does cognitive ability play?
There may still be other important differences between
obese and non-obese people that we have not accounted
for. One may suspect that obesity is simply picking up
some differences in cognitive ability between the obese
and non-obese. We, therefore, add controls for cognitive
ability, measured through results on test scores in math
and reading at ages 7 and 11. In addition, we control for
social maladjustment at ages 7 and 11. The results are
shown in the columns (IV) of Table 2. Adding cognitive
M. Lindeboom et al. / Economics and Human Biology 8 (2010) 309–319
316
Table 3
Correlation in obesity status between the child and the child’s mother and father.
(I) Raw association
Males
Age
Age
Age
Age
Age
Age
7
11
16
23
33
42
(II) Association with controls
Females
Males
Females
Mother
Father
Mother
Father
Mother
Father
Mother
Father
0.012*
0.042***
0.028***
0.030***
0.059***
0.131***
0.043***
0.036***
0.033***
0.063***
0.199***
0.187***
0.048***
0.041***
0.046***
0.101***
0.151***
0.193***
0.029**
0.030***
0.049***
0.033**
0.108***
0.140***
0.013**
0.042***
0.026***
0.025***
0.044**
0.124***
0.044***
0.036***
0.031***
0.059***
0.192***
0.176***
0.050***
0.040***
0.047***
0.094***
0.133***
0.165***
0.028**
0.029***
0.045***
0.029**
0.098***
0.128***
(II) Basic controls, parental background, cognitive ability, and parental inputs.
*
p < 0.1.
**
p < 0.05.
***
p < 0.01.
ability only has a slight effect on the employment
estimates. For males, however, the employment penalty
obtained when obesity is measured at age 33 is now
reduced in magnitude and is only significant at the 10%
level.
4.2.4. Is it the parental inputs?
Finally, we consider the importance of a number of
parental inputs, that may not have been picked up by the
family background variables. We consider leisure activities, such as taking the child frequently for walks, to
swimming pools, and on outings at age 11. Moreover, we
include controls for parental educational inputs, measured
through whether the mother and the father read frequently to the child. Finally, we control for mothers
smoking during pregnancy, whether or not the mother
breastfed, the existence of alcohol problems within the
family, low birth weight, and the existence of any domestic
tension within the household.
As seen in the columns (V) of Table 2, the associations
between obesity and employment do hardly change. It
should be noted that very few of these early parental
inputs show any significant effect on later employment
(results on request). In sum, differences in family background, cognitive ability, and parental inputs by obesity
status do not seem to fully explain why obese people fare
worse on the labor market at later ages.
accounting for non-genetic factors pertaining to the family
background of the child. If the association in obesity is
unaffected when accounting for potentially important
environmental influences during childhood and adolescence, this provides at least suggestive evidence that the
association is mainly due to genetic factors.
In the first four columns of Table 3, the raw parent–
child association in obesity at various ages are shown,
separately for females and males and for mothers and
fathers. Each row thus represents a separate regression
clearly, there is a strong association in obesity between the
parent and the child at all ages, for both males and females.
The associations tend to be stronger at older ages, which is
not that surprising, since at these ages, the main
respondent is closer to the age at which the height and
weight of the mother and the father were assessed.
In columns five to eight of Table 3, the same
associations are again examined but this time controlling
for all observed demographic, socioeconomic, environmental and behavioral characteristics described in the
preceding section. The results show that the parent–child
associations remain very similar. Moreover, most of the
included control variables are not significant in explaining
obesity at later ages (results available on request). This is in
line with the results from previous studies, suggesting that
the association in obesity between biological relatives is
mainly due to genetic factors and that factors related to a
common environment plays a small or no role.
4.3. Instrumental variable estimation
Even though the richness of the NCDS data allow us to
control for many previously unmeasured factors, such as
cognitive abilities and parental investments, important
factors may still remain unmeasured. Moreover, as
previously discussed, reverse causality running from labor
market outcomes to obesity and BMI may bias our results.
We will, therefore, resort to instrumental variables
estimation, using our indicator of the obesity status of
the mother and the father of the respondent as instruments.
4.3.1. Parent–child association in obesity and the role of
family background
First, we will examine to what extent the association in
obesity between the parent and the child is affected when
4.3.2. Is the mother–child relationship in obesity different for
adopted children?
Next, we turn our attention to differences in the parent–
child association in obesity between adopted and nonadopted children. The NCDS records whether the main
respondent was adopted. If the parent–child association is
only due to genetics, we would expect no relationship in
the obesity status of adopted children and their adoptee
parents. For the analysis to make sense, we must first make
sure that the allocation of adopted children is made in a
close to random manner. For the NCDS this has been
established by Sacerdote (2002).
The sample of adopted children is small, only 79, and
the results are, therefore, not likely to be very strong. Yet,
they may provide at least some suggestive evidence,
pointing in the same direction as our previous results. We
M. Lindeboom et al. / Economics and Human Biology 8 (2010) 309–319
317
Table 4
Correlation in body size between adopted and biological children obesity status and the parents obesity status.
Ages
Age 7
Mother obese
Father obese
Non-adopted (mother)
Non-Adopted (father)
Non-adopted (mother)*mother obese
Non-adopted (father)* father obese
Observations
R2
0.017
0.021
0.012
0.013
0.045
0.056
10,565
0.01
(0.082)
(0.081)
(0.029)
(0.023)
(0.082)
(0.081)
Age 11
0.029
0.028
0.003
0.014
0.071
0.058
11,487
0.01
Age 16
(0.065)
(0.074)
(0.025)
(0.020)
(0.065)
(0.074)
0.019
0.010
0.019
0.010
0.057
0.051
8827
0.01
(0.120)
(0.085)
(0.028)
(0.023)
(0.119)
(0.085)
Robust standard errors in parentheses. **p < 0.05, ***p < 0.01.
*
p < 0.1
run regressions, pooling the samples of adopted and
natural children, and include interaction terms between
the dummy variable indicating not being an adopted child
and the variable measuring the obesity status of the parent.
If the parent–child association is due to genetics, we would
expect the interaction effects to be positive, but the main
effect, measuring the relationship for adopted children, to
be close to zero or negative for adopted children.
To maximize the number of adopted children, we will
only consider the respondents at ages 7, 11, and 16. At later
ages, due to attrition, the sample of adopted children
becomes very small. The early ages are the most relevant
since this is the period where most children and parents
share a common environment.
In Table 4 OLS results for the full sample at the ages 7,
11, and 16 are shown. The table shows the coefficients of
the variables indicating parental obesity, the dummy
variables indicating not being an adopted child, and the
interaction terms between the former and the latter
variables.4 The results show that the interaction terms are
positive at ages 7, 11, and 16. The magnitude of the main
effect of mother’s and father’s obesity suggests an
association that is either close to zero or negative for
adopted children. This is found for all the ages considered
in the analysis. This is in line with what we would expect if
genetics play the major role for the association in obesity
between parents and children. The sample of adopted
children is small, however, and the interaction effects
never reach statistical significance. We are, therefore, not
able to draw firm conclusions from this exercise, but we
note that the results at least point to the direction of no
association or a negative association in obesity between
adopted children and their adoptee parents.
The exercises above, together with the evidence from
previous twin and adoption studies, gives some credibility
to the claim that the obesity of a parents predicts
genetically induced variation in the obesity of the child.
If true, non-genetic factors that are common to the child
and the parent play little role for obesity, meaning also that
those factors do not predict both obesity and labor market
success, which would invalidate our instrument.
4
We ran the analysis both with and without controls for parental
background and environmental influences but the results were similar.
The results presented in Table 4 are without controls.
Table 5
Marginal effects and standard errors from IV employment regressions for
males and females.
Employment
Obese 42
F-stat. Ist stage
Sargan test (p-val)
n
Obese 33
F-stat. 1st stage
Sargan test (p-val)
n
Males
Females
0.113 (0.086)
35.41
0.11
4185
0.149 (0.103)
32.08
0.77
3543
0.014 (0.129)
34.79
0.19
4147
0.082 (0.176)
24.60
0.13
3694
Basic controls, parental background, cognitive ability, and parental
inputs. *p < 0.1, **p < 0.05, and ***p < 0.01.
4.3.3. IV results
Next, we perform instrumental variables analyses. The
results are summarized in Table 5. To preserve space, we
only show the marginal effects of the obesity variable.
As shown in the table, the IV estimates are very
different from the OLS estimates. For males, the association
between obesity and employment is now positive but
imprecisely measured and insignificant. The instruments
predict well, with F-statistics above 10 in the first-stage
regressions. Similar results are found for females, where
the coefficients of obesity at various ages are now positive
and insignificant. The previously found negative and
significant association between obesity and employment
of women has now completely vanished. Also among
women, the instruments predicts obesity well. While we
cannot exclude that our exclusion restrictions are valid in
the employment regressions, based on the Sargan tests, it
should be noted that the p-values are rather low in two out
of the four specifications, which may be a cause of concern.
However, even a rejection of the overidentification test
statistic does not necessarily mean that the instruments
are invalid. If there is heterogeneity in the effects, different
instruments may give different causal effects. IV measures
the treatment effect for the compliers, which may be
different subsamples for different instruments. Thus, if the
compliers are different when using fathers’ obesity as an
instruments compared to when when using the mothers’
obesity status, the overidentification test statistic may be
rejected (see for instance, Angrist and Pischke, 2009, for a
discussion). One should, therefore, be careful in interpret-
M. Lindeboom et al. / Economics and Human Biology 8 (2010) 309–319
318
Table 6
Marginal effects and robust standard errors from first-difference
employment regressions for males and females.
DEmployment
DObesity
Observations
Males
Females
0.026* (0.015)
3470
0.033 (0.028)
3578
Controls for change in marital status and smoking status. Robust standard
errors in parentheses. *p < 0.1, **p < 0.05, ***p < 0.01.
ing the results from an overidentification test, such as the
Sargan test. Our result raises the question why one should
expect different effects when using fathers’ or mothers’
obesity status as instruments if the instruments only pick up
genetic information. The parents of the child both provide
50% of the child’s genes and this alternative interpretation of
the overidentification test may, therefore, also suggest that
our instruments pick up something more than pure genetics.
In relation to this, it could also be noted that the results in
Table 3, i.e. the associations between parental and child
obesity differ between mothers and fathers.
4.4. Changes in obesity and employment
If the genes that determine obesity are the same or are
correlated with the genes that determine employment,
both our OLS and IV estimates may still be biased. To the
extent that cognitive ability, for instance, is genetically
determined it is certainly not farfetched to assume that
both employment and obesity may be affected by it. In the
case of cognitive ability, we are able to control for it by
including education and test scores at childhood, but for
other potentially genetically determined characteristics
we may be less successful.
We, therefore, utilize the panel feature of the NCDS in
order to examine changes in employment status and
obesity between the waves. Estimating our regressions in
first differences, we are able to remove some of the
unobserved heterogeneity that may plague both the OLS
and IV estimates. Only time-invariant unobserved heterogeneity will cancel out. It should be noted that some of the
important unmeasured factors in our case are indeed timeinvariant, such as genetic upset.
Table 6 shows the results from the first-difference
equation. In this specification, we are analyzing changes in
employment between age 42 and 33 as a function of
changes in obesity between ages 42 and 33. In our sample
of males, 9% changes employment status across the waves.
The corresponding percentage of females is 30%. Since
most of the control variables are constant over time, they
will drop out from the analysis, but we allow for changes in
marital status and in smoking. For males, the results show
a slight positive association between changes in obesity
and employment that is significant at the 10% level. For
females, the association is negative but not significant.
5. Discussion and conclusions
We had two main aims with this paper. The first was to
study the causal effect of obesity on employment using (1)
parental obesity as an instrument and (2) a fixed effects
approach. The second was to carefully assess the validity of
using the body size of a biological relative as an instrument
for one own’s body size. This latter aim was motivated by
the growing literature that uses instruments supposed to
carry genetic information.
Starting with our first aim, we obtained no results
suggesting a strong causal effect of obesity on employment. We started by showing that there exist large
differences in employment probabilities between obese
and non-obese men and women. We also showed that
these associations did not disappear when controlling for a
wealth of potentially important factors pertaining to
family background, cognitive ability, and inputs during
childhood and adolescence. When we instrumented the
individual’s obesity status with that of the respondent’s
mother and father, no significant effect remained, however. This result was confirmed in panel data regressions
on first differences in employment and obesity. With this
strategy, we removed unmeasured time-invariant heterogeneity, such as the influence of common genes.
Our results confirm those of Cawley (2000a,b) and
Norton and Han (2008), where no effect of obesity on
employment among US men and women was obtained, but
contrast to those obtained by Morris (2007) for the UK. One
potential reason for the diverging findings is that
genetically induced variation in obesity was used to
identify the effect in this study and in the studies by
Cawley (2000a,b) and Norton and Han (2008), whereas
Morris (2007) relied on variation in obesity induced by
local area variation in average BMI. Since different
instruments are being used, the compliant subpopulation
may very well differ between the studies and different
Local Average Treatment Effects will be produced.
Regarding our second aim, assessing the validity of so
called ‘‘genetic’’ instruments, we obtained some mixed
results. Our tests of the instruments suggested that it is
mainly genetic variation which causes intergenerational
correlation in obesity, while environmental factors play
less of a role. This is in line with evidence from a large
number of studies in medicine, where no common
household effects are obtained. The instruments passed
our tests for overidentifying restrictions, but the p-values
were somewhat low in some specifications. This does not
need to imply that our instruments were not valid, since a
rejection of the Sargan test statistic could also result from
treatment heterogeneity between the instruments. Such
an interpretation would, on the other hand, raise the
question as to why the compliers would be different when
using fathers’ obesity or mothers’ obesity as instruments.
Since both parents provide 50% of the child genes, one
would expect similar results irrespective of what instrument is used. Future studies should further assess the
validity of using the weight of a biological relative as an
instrument.
To conclude, if obesity has a causal effect on some labor
market outcomes, there may be large gains at both the
individual and societal level from more efficient obesity
treatments. Indeed, previous studies have suggested
relatively large penalties from being obese. If obesity is
simply picking up some other, unmeasured trait, treat-
M. Lindeboom et al. / Economics and Human Biology 8 (2010) 309–319
ments and/or policies aimed at reducing obesity will not
have the intended effects on labor market outcomes. It is,
therefore, of great importance to increase the knowledge
about the causal effect of obesity on labor market success.
While our results do not imply that obesity affects
employment, these results were obtained in a UK context
and using a particular type of instrument. Since the
literature on the effect of obesity on employment is rather
limited, it should be of great interest to analyze the
corresponding effects in other countries, where the labour
markets look different, and using different sources of
variation in obesity.
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